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SimpleImputer

Converter
DashAI.back.converters.scikit_learn.SimpleImputer

Fill missing values using a simple univariate per-column strategy.

Each feature is imputed independently using one of four strategies:

  • "mean" — replace missing values with the column mean (numeric only).
  • "median" — replace with the column median (numeric only).
  • "most_frequent" — replace with the most common value (works with strings and numeric data).
  • "constant" — replace with a fixed fill_value supplied by the user.

Columns with all-missing values are handled according to the keep_empty_features flag. When add_indicator=True, a MissingIndicator binary matrix is stacked onto the output. All output columns are typed as Float64 in DashAI regardless of the original column type.

Wraps sklearn.impute.SimpleImputer.

References​

Parameters​

strategy : string, default=mean
The imputation strategy.
fill_value, default=None
The value to replace missing values with.
use_copy : boolean, default=True
If True, a copy of X will be created.
add_indicator : boolean, default=False
If True, a MissingIndicator transform will stack onto output.
keep_empty_features : boolean, default=False
If True, empty features will be kept.

Methods​

get_output_type(self, column_name: str = None) -> DashAI.back.types.dashai_data_type.DashAIDataType

Defined on SimpleImputer

Return the DashAI data type produced by this converter for a column.

Parameters

column_name : str, optional
Not used; all output columns share the same type. Defaults to None.

Returns

DashAIDataType
A Float type backed by pyarrow.float64().

changes_row_count(self) -> 'bool'

Defined on BaseConverter

Indicate whether this converter changes the number of dataset rows.

Returns

bool
True if the converter may add or remove rows, False otherwise.

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> DashAI.back.converters.base_converter.BaseConverter

Defined on SklearnWrapper

Fit the scikit-learn transformer to the data.

Parameters

x : DashAIDataset
The input dataset to fit the transformer on.
y : DashAIDataset, optional
Target values for supervised transformers. Defaults to None.

Returns

BaseConverter
The fitted transformer instance (self).

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type
The converter class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]
Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

transform(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on SklearnWrapper

Transform the data using the fitted scikit-learn transformer.

Parameters

x : DashAIDataset
The input dataset to transform.
y : DashAIDataset, optional
Not used. Present for API consistency. Defaults to None.

Returns

DashAIDataset
The transformed dataset with updated DashAI column types.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.